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AKTU Artificial Intelligence Lab (BCS751H) Syllabus PDF – B.Tech 4th Year
Artificial Intelligence Lab BCS751H
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- Semester 7
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Official AKTU syllabus of Artificial Intelligence Lab (BCS751H) for B.Tech 4th Year – 1 credits, L-T-P 0-0-2. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Artificial Intelligence Lab
Download PDFBCS751H · 1 credits · L-T-P 0-0-2
Course Outcomes
At the end of course, the student will be able to:
| CO 1 | Understand and apply basic search algorithms and intelligent problem-solving approaches in Python. | K3 |
| CO 2 | Design and implement logic-based knowledge representations and reasoning using Python and Prolog. | K4 |
| CO 3 | Use Natural Language Toolkit (NLTK) for basic NLP tasks like stemming, tagging, and classification. | K4 |
| CO 4 | Analyze and implement basic AI game strategies like Minimax and Alpha-Beta pruning. | K4 |
| CO 5 | Apply AI techniques in real-world tasks such as text processing, planning, or constraint satisfaction. | K3 |
Course Content
- Implement Breadth First Search (BFS) for a given graph or maze.
- Implement Depth First Search (DFS) for a tree or graph structure.
- Solve the 8-Puzzle Problem using A* Search Algorithm.
- Implement Hill Climbing Algorithm for numerical optimization or pathfinding.
- Implement Simulated Annealing Algorithm for constraint-based search problems.
- Solve Water Jug Problem using state-space search (BFS or DFS).
- Write Prolog programs to define family relationships using predicates.
- Implement 4-Queens Problem in Prolog using backtracking.
- Implement Unification Algorithm in Python or Prolog.
- Implement Forward and Backward Chaining in a rule-based system (manual or code-based).
- Demonstrate Resolution in Propositional Logic through a basic example (e.g., proving a theorem).
- Remove punctuation and stop words from a paragraph using nltk.
- Perform stemming and lemmatization on user-input text.
- Apply POS (Part of Speech) tagging using NLTK on a given sentence.
- Build a simple text classifier using NLTK (e.g., classify messages as spam/ham).
- Implement Tic-Tac-Toe game with a basic AI opponent.
- Implement Min-Max (Minimax) Algorithm for decision making in turn-based games.
- Enhance the game with Alpha-Beta Pruning to optimize Min-Max.
- Simulate a Vacuum Cleaner Agent that intelligently cleans a 2D environment.
- Build a simple chatbot using rules or pre-trained logic (can use regex or basic intent matching).
- Design a Constraint Satisfaction Problem solver, e.g., Sudoku, or Map Coloring.
- Perform simple Bayesian reasoning for a probability-based decision problem (e.g., medical diagnosis). Instructions to the Instructor:
- The instructor may add/delete/modify/tune experiments depending on syllabus coverage and availability of tools.
- All experiments should preferably be implemented using Python (with NLTK and standard libraries) and SWI-Prolog for logic programming tasks.
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